EXPERTS warn that "the substitution of machinery for human labour" may "render the population redundant". They worry that "the discovery of this mighty power" has come "before we knew how to employ it rightly". Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a "Terminator"-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the "machinery question".

In this paper, we present BigDL, a distributed deep learning framework for Big Data platforms and workflows. It is implemented on top of Apache Spark, and allows users to write their deep learning applications as standard Spark programs (running directly on large-scale big data clusters in a distributed fashion). It provides an expressive, "data-analytics integrated" deep learning programming model, so that users can easily build the end-to-end analytics + AI pipelines under a unified programming paradigm; by implementing an AllReduce like operation using existing primitives in Spark (e.g., shuffle, broadcast, and in-memory data persistence), it also provides a highly efficient "parameter server" style architecture, so as to achieve highly scalable, data-parallel distributed training. Since its initial open source release, BigDL users have built many analytics and deep learning applications (e.g., object detection, sequence-to-sequence generation, visual similarity, neural recommendations, fraud detection, etc.) on Spark.

Many natural language processing tasks require dealing with Named Entities (NEs) in the texts themselves and sometimes also in external knowledge sources. While this is often easy for humans, recent neural methods that rely on learned word embeddings for NLP tasks have difficulty with it, especially with out of vocabulary or rare NEs. In this paper, we propose a new neural method for this problem, and present empirical evaluations on a structured Question-Answering task, three related Goal-Oriented dialog tasks and a reading-comprehension-based task. They show that our proposed method can be effective in dealing with both in-vocabulary and out of vocabulary (OOV) NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and Out-of-vocabulary (OOV) versions of the CBT test set which will be made publicly available online.

We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.

In this project I have attempted to create supervised learning models to assist in classifying certain employee data. I pre-processed the data by removing one outlier and producing new features in Excel as the data set was small at 1056 rows. Some categorical features were also converted to numeric values in Excel. For example, Gender was originally "M" or "F", which was converted to 0 and 1 respectively. I also removed employee number as it provides no value as a feature and could compromise privacy.

As a data scientist who has been in the profession for several years now, I am often approached for career advice or guidance in course selection related to machine learning by students and career switchers on LinkedIn and Quora. Some questions revolve around educational paths and program selection, but many questions focus on what sort of algorithms or models are common in data science today. With a glut of algorithms from which to choose, it's hard to know where to start. Courses may include algorithms that aren't typically used in industry today, and courses may exclude very useful methods that aren't trending at the moment. Software-based programs may exclude important statistical concepts, and mathematically-based programs may skip over some of the key topics in algorithm design. I've put together a short guide for aspiring data scientists, particularly focused on statistical models and machine learning models (supervised and unsupervised); many of these topics are covered in textbooks, graduate-level statistics courses, data science bootcamps, and other training resources (some of which are included in the reference section of the article).

Artificial intelligence has gone mainstream. It's already integrated into our everyday lives, with realistic voices responding to questions and requests. For a while now, we have been able to get reminders for appointments, encouragement for fitness routines, and remote control over household lights and appliances. But can these devices be useful in schools? In addition to being sources of factual information, can they enhance thought-provoking activities?

This section contains tutorials demonstrating how to do specific tasks in TensorFlow. If you are new to TensorFlow, we recommend reading the documents in the "Get Started" section before reading these tutorials. These tutorials focus on machine learning problems dealing with sequence data. These tutorials demonstrate various data representations that can be used in TensorFlow. Although TensorFlow specializes in machine learning, the core of TensorFlow is a powerful numeric computation system which you can also use to solve other kinds of math problems.

The importance of training has long been recognized, but the link between training and performance is often hard to quantify. Typically, training and learning at work have taken place away from the workplace. Training has been the preserve of the classroom. Recently, classroom training may have been replaced or augmented by the digital content in a Learning Management System (LMS). In theory, the LMS makes learning more accessible and available.

He is a co-inventor of the UNIX operating system and was a graduate student at MIT's pioneering Computer Science & Artificial Intelligence Lab. After graduating cum laude in Physics from Harvard University, I went on to MIT for my graduate work. I started there in 1959, spending the first year and a half completing coursework for my Ph.D. qualifying exams. With that milestone behind me it was time to choose my Master's thesis topic. It was then that I decided to focus my thesis on the area of artificial intelligence.